Entity Linking with Convolutional Neural Network
| atmire.migration.oldid | 5049 | |
| dc.contributor.advisor | Alhajj, Reda | |
| dc.contributor.author | Xu, Shunyi | |
| dc.contributor.committeemember | Rokne, Jon | |
| dc.contributor.committeemember | Fapojuwo, Abraham | |
| dc.date.accessioned | 2016-10-04T15:42:54Z | |
| dc.date.available | 2016-10-04T15:42:54Z | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016 | en |
| dc.description.abstract | Entities are real world objects such as persons, places, or events that appear in natural language text such as web pages, news, and journals. Entity Linking, a nascent field in Natural Language Processing, is the task of linking entities in text to their referent entries in a Knowledge Base (KB), which is a repository of information such as Wikipedia. There’s a huge application of entity linking in automatic knowledge base population, prevention of identity crimes, etc. It can also provide background information about unfamiliar concepts during document reading, rendering a smooth and joyful reading experience without frequent “context switch”. This thesis taps into the power of convolutional neural network, and proposes an architecture that makes use of deep learning layers, convolution, max pooling, and fully-connected neurons with dropout to approach the problem of entity linking. Based on a pre-trained word2vec word embedding and another ad-hoc trained layer of word representation, we were able to outperform previous state-of-art models, which handcrafted a large number of features, by a modest margin. Visualization of the neural network is also provided in order to understand what happens under the hood. Our experiment showed that it clearly captured the desired features, indicating the efficacy of neural network in dealing with entity linking. | en_US |
| dc.identifier.citation | Xu, S. (2016). Entity Linking with Convolutional Neural Network (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25915 | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/25915 | |
| dc.identifier.uri | http://hdl.handle.net/11023/3375 | |
| dc.language.iso | eng | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.institution | University of Calgary | en |
| dc.publisher.place | Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject | Computer Science | |
| dc.subject.classification | NLP | en_US |
| dc.subject.classification | Convolutional Neural Network | en_US |
| dc.subject.classification | Entity Linking | en_US |
| dc.title | Entity Linking with Convolutional Neural Network | |
| dc.type | master thesis | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Master of Science (MSc) | |
| ucalgary.item.requestcopy | true |